It is estimated that 34% of adults in the United States are overweight and an additional 32% have obesity. Behavioral and pharmacologic treatments for obesity targeting specific pathways have met with limited long- term success. In contrast, Roux-en-Y gastric bypass (RYGB) provides effective and durable weight loss. The mechanisms underlying the dramatic clinical response of this procedure are not well understood, but emerging evidence indicates that the observed response is due to altered neuronal and hormonal regulation of energy intake, energy expenditure, metabolic efficiency, and hedonic pathways, rather than to the mechanical effects of surgery. These observations implicate a strong biological, and possibly genetic, contribution to weight loss after surgery. Identification of factors associated with weight loss after RYGB could help provide insight into the mechanisms of action of weight loss after this procedure. Clinical factors associated with weight loss after RYGB have been identified; however, these factors have only been able to explain a fraction of the total variation in the resulting weight loss. We recently conducted an analysis that suggests that genetic factors could explain a large percentage of the remaining variability. The overall goal of this research proposal is to determine the specific genetic factors involved and to identify a combination of genetic, clinical, and circulating biomarker factors that reproducibly predict weight loss after RYGB. We hypothesize that there are reproducible genetic and clinical predictors of weight loss after RYGB, and propose to test this hypothesis through two Specific Aims. The first Specific Aim is to [identify genetic associations through an analysis of genotyped and imputed genetic loci,] and to then replicate these findings, using two additional cohorts totaling 1472 RYGB patients. The second Specific Aim is to identify multivariable-adjusted genetic, clinical, and circulating biomarker factors that reproducibly predict weight loss after RYGB. We will first identify predictors that pass an initial univariate screen of p < 0.10 for clinical predictors and p < 5.0*10-5 for genetic predictors in 1172 RYGB patients; potential predictors that reach these criteria will be entered into a stepwise selection procedure to identify significant multivariable-adjusted predictors (p<0.05). Because we are testing a large number of candidate predictors, we will then replicate these results in two new cohorts totaling more than 2000 additional patients using a two-stage approach. First, we will re-analyze the candidate predictors using a stepwise selection procedure in 1100 new RYGB patients. Second, we will test the model derived in this first stage as a whole in two new cohorts totaling approximately 900 RYGB patients. Identification of a reliable set of genetic and clinical predictors could provide important clues to the mechanism of action of this therapy. In addition, these predictors could be used to develop clinical tools to identify those patients who will likely benefit most from surgery, thus further improving the risk:benefit profile for this highly effective yet invasive treatment.